基于双树复小波变换的轴承复合故障诊断研究

Study on compound fault diagnosis of rolling bearing based on dual-tree complex wavelet transform

针对滚动轴承复合故障信号特征难以分离的问题,提出将双树复小波变换和独立分量分析(ICA)结合的故障诊断方法。该方法首先将非平稳的故障信号通过双树复小波变换分解为若干不同频带的分量;由于各个分量存在一定的频率混叠,对故障信号特征提取有很大的干扰,进而引入ICA对各个分量所组成的混合信号进行盲源分离,从而尽可能消除频率混叠;最后对从混合信号中分离出来的独立分量信号进行希尔伯特包络解调,即可实现对复合故障特征信息的分离和故障识别。实验结果表明,该方法有效地分离和提取了滚动轴承复合故障的特征信息,验证了方法的可行性和有效性。

Aiming at the difficulty of separating the fault feature from compound rolling bearing fault signal, a new fault diagnosis method is proposed based on dual-tree complex wavelet transform （DT-CWT） and independent com- ponent analysis （ICA）. Firstly, DT-CWT is used to decompose the non-stationary fault vibration signal into several components with different frequency bands. Because frequency aliasing exists in the components, this problem dis- turbs the feature extraction of the fault signal. Then, ICA is introduced to perform blind source separation on the mixed signal consisting of various components to eliminate the frequency aliasing as far as possible. Finally, Hilbert envelope decomposition is performed on the independent signal components separated from the mixed signal. Thus the compound fault feature information can be separated, and the fault identification is achieved. The experiment re- suits show that the proposed method can effectively separate and extract the feature information of the compound roll- ing bearing faults, which verifies the feasibility and effectiveness of the proposed method.